Development of a predictive nomogram for in-hospital death risk in multimorbid patients with hepatocellular carcinoma undergoing Palliative Locoregional Therapy

Patients diagnosed with hepatocellular carcinoma (HCC) often present with multimorbidity, significantly contributing to adverse outcomes, particularly in-hospital mortality. This study aimed to develop a predictive nomogram to assess the impact of comorbidities on in-hospital mortality risk in HCC patients undergoing palliative locoregional therapy. We retrospectively analyzed data from 345 hospitalized HCC patients who underwent palliative locoregional therapy between January 2015 and December 2022. The nomogram was constructed using independent risk factors such as length of stay (LOS), hepatitis B virus (HBV) infection, hypertension, chronic obstructive pulmonary disease (COPD), anemia, thrombocytopenia, liver cirrhosis, hepatic encephalopathy (HE), N stage, and microvascular invasion. The model demonstrated high predictive accuracy with an AUC of 0.908 (95% CI: 0.859–0.956) for the overall dataset, 0.926 (95% CI: 0.883–0.968) for the training set, and 0.862 (95% CI: 0.728–0.994) for the validation set. Calibration curves indicated a strong correlation between predicted and observed outcomes, validated by statistical tests. Decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility in predicting in-hospital mortality. This nomogram offers a practical tool for personalized risk assessment in HCC patients undergoing palliative locoregional therapy, facilitating informed clinical decision-making and improving patient management.


Participants
Eligibility criteria included: (1) presence of comorbidities at admission; (2) primary diagnosis upon admission; and (3) classification of the main diagnosis according to the International Classification of Diseases for Oncology (ICD-O) codes (M8170/3, M8171/3, M8172/3, M8173/3, M8174/3, and M8175/3) alongside the International Classification of Diseases (10th edition) codes (C22.000).The inclusion criteria were as follows: (1) a definitive diagnosis of HCC at discharge coupled with a minimum hospitalization period of 24 h and (2) the administration of one of three treatment modalities during hospitalization-TACE, RT, or RFA.The exclusion criteria for patients were as follows: (1) essential information was unavailable; (2) the patient required admission to the intensive care unit; and (3) an absence of prior medical history data.The data collected were demographic data, patient comorbidities, and treatment outcomes, and they were extracted from electronic medical records.

Outcome
The primary outcome of interest is in-hospital mortality, defined as death occurring during hospitalization, which is coded based on the patient's discharge disposition (alive or deceased).We selected variables associated with in-hospital mortality that are available from the electronic medical record database.These include gender, age, readmissions, smoking history, alcohol consumption history, medical history, TNM stage, tumor characteristics, and modes of interventional treatment (e.g., TACE, RT, RFA).

Predictors
The predictors used in developing the nomogram were clearly defined and included demographic variables (gender, age), clinical parameters (length of stay (LOS), smoking history, alcohol consumption history), comorbidities (HBV status, hypertension, COPD, asthma, CKD, anemia, thrombopenia, ascites, pneumonia, liver cirrhosis, hepatic encephalopathy (HE)), and tumor characteristics (TNM stage, portal vein invasion, microvascular invasion).All variables were collected from the electronic medical record system.Comorbidities were identified based on the medical history at admission and discharge diagnoses.If a patient had a documented history of a chronic disease or if the discharge diagnosis included the chronic disease, the corresponding variable was coded as 1.If there was no report or discharge diagnosis of the disease, the variable was coded as 0. Tumor characteristics were similarly coded: the presence of specific tumor-related conditions such as portal vein invasion or microvascular invasion was assigned a value of 1, and their absence was assigned a value of 0. This method ensured a comprehensive and standardized collection of predictor variables, reflecting the true clinical status of each patient.
We used CT or MRI imaging techniques to indirectly assess microvascular invasion (MVI) in liver cancer patients 18 .All imaging data were sourced from our hospital's imaging database, including contrast-enhanced CT and enhanced MRI.During image loading and preprocessing, two experienced radiologists independently evaluated the imaging features, ensuring they were blinded to the patients' clinical information.The evaluated imaging features included nonsmooth tumor margins, peritumoral enhancement, and TTPVI (Two-Trait Predictor of Venous Invasion), which comprises internal arteries and a low-density halo (or low-signal halo).
The presence and combination of these features were used to predict the likelihood of MVI, thereby supporting clinical decision-making.
To reduce bias, the assessment of predictors was blinded to the outcome of in-hospital mortality, and vice versa.The clinical staff responsible for recording the patients' medical history and comorbidities were unaware of the study's outcome measurements, which were determined independently.Additionally, those analyzing the data and building the predictive models were blinded to the patients' clinical characteristics during the initial data collection phase.This ensured that the predictor variables were assessed independently of the outcomes, thus minimizing potential biases in the data analysis.
The candidate variables for our prognostic analysis were chosen based on their availability in the hospital records and their clinical relevance to hepatocellular carcinoma (HCC) patient outcomes.These variables are detailed in Table 1.

Sample size
Currently, the academic community lacks a unified approach for deriving risk prediction models and determining requisite sample sizes for validation studies.In the context of model derivation, a general recommendation posits the necessity of a minimum of 10 events per candidate variable 19 .In our study, we incorporated 29 candidate variables within the framework of a binary logistic regression model analysis.Adhering to these guidelines, the minimal sample size for robust validation was calculated to be at least 290 individuals 19 .

Missing data
In this study, missing data were present in two variables: AFP and CEA, with missing rates of 5.8% and 10.4%, respectively.To ensure the completeness and reliability of the statistical analysis, we used multiple imputation to handle the missing data, specifically employing the predictive mean matching (PMM) method.The imputation process was implemented in R using the "mice" package and the "mice" function for imputation.While we aimed to include all relevant prognostic variables used in clinical practice, certain liver function tests (e.g., ALT, AST, TBIL, albumin) had more than 20% missing values.This high rate of missing data was primarily due to variations in clinical practice and incomplete documentation in medical records.As a result, we were unable to use multiple imputation methods to address these missing values without risking significant bias in our analysis.We ensured that all other available and relevant data were included to build a robust prognostic model.

Statistical analysis methods
In this study, a total of 345 patients were included in the overall dataset and randomly divided into a training cohort of 242 patients and a validation cohort of 103 patients in a 7:3 ratio.Statistical analysis was conducted using R software (version 3.6.3;available at https:// www.R-proje ct.org) and SPSS version 25.0 (IBM, Armonk, NY, USA).We expressed categorical variables as frequencies (percentages) and continuous variables as means (standard deviations).To evaluate differences in baseline characteristics, we used Student's t-test or the nonparametric Mann-Whitney U test for continuous variables, and the Chi-square test or Fisher's exact test for categorical variables.In addition to comparing differences between the mortality group and the overall dataset, we also compared categorical variables between the overall set, training set, and validation set using the Chi-square test or Fisher's exact test, and continuous variables using one-way ANOVA or the Kruskal-Wallis test, as appropriate.
The 'glmnet' package facilitated the identification of factors influencing the risk of in-hospital mortality using the least absolute shrinkage and selection operator (LASSO) method, which is preferred over univariate logistic regression for variable selection in predictive modeling.LASSO was chosen for its ability to handle highdimensional data and multicollinearity by shrinking some coefficients to zero, thus performing variable selection and regularization simultaneously.We selected the optimal lambda (λ) value with the minimal standard error through cross-validation.
Multivariate logistic regression analysis was used to identify independent clinical predictors associated with in-hospital mortality, considering P values < 0.05 to be significant.A nomogram based on these multivariate risk factors was created to predict in-hospital mortality.The nomogram's predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC-ROC), and calibration curves were generated to align the predicted probabilities with the observed probabilities.
For validation, a bootstrap method with 1,000 resamples was applied to the in-hospital mortality nomogram, yielding a bias-corrected C-index.Decision curve analysis (DCA) was used to assess the clinical utility of the in-hospital mortality nomogram by quantifying the net benefits at various threshold probabilities in the study cohort.Net benefits were computed by offsetting the proportion of false positives against true positives while considering the relative harms of foregoing intervention against those of unnecessary intervention.

Risk groups
Details on how risk groups were created were based on the stratification of predicted probabilities derived from the nomogram.High-risk groups were defined as patients with predicted probabilities above a certain threshold, while low-risk groups had probabilities below that threshold.To determine the optimal threshold for defining high-risk and low-risk groups, we used a combination of clinical relevance and statistical methods.Initially, the Youden Index, which maximizes the sum of sensitivity and specificity, was calculated using the training dataset to identify the threshold that provides the best balance between true positive and false positive rates.This threshold was then validated using the testing dataset to ensure its robustness and clinical relevance.The threshold for defining high-risk and low-risk groups was ultimately set at 0.20, based on these considerations.

Development vs. validation
In the development phase, the model was constructed using the training dataset.The nomogram was developed based on multivariate logistic regression analysis, incorporating predictors identified through LASSO regression.The model's performance was assessed using the training data.In the validation phase, the model's performance was evaluated using the validation dataset.Differences between the development and validation datasets were assessed in terms of setting, eligibility criteria, outcome, and predictors.This step ensured the model's robustness and generalizability across different patient populations and clinical settings.

Ethics statement
For the publication of any potentially identifiable images or data included in this article, written informed consent was duly obtained from the respective individual(s).This procedure adheres to ethical standards for the protection of personal privacy and aligns with the guidelines for the dissemination of identifiable research data.

Patient characteristics
From January 2015 to December 2022, 392 HCC patients underwent interventional therapy.After applying exclusion criteria, 345 patients were eligible for analysis (Fig. 1).The average age of patients in the survival and the in-hospital death groups were 62.

Variable selection
To identify the most relevant predictors of in-hospital mortality among patients with HCC following interventional therapy, we employed LASSO regression.LASSO regression is particularly effective in handling datasets with numerous predictors and potential multicollinearity, as it performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model.Using the training dataset, we initially considered 29 potential predictors, including demographic factors, comorbidities, and clinical parameters.The LASSO regression model identified 21 significant predictors with non-zero coefficients (Fig. 2 A, B).These predictors included gender, LOS, smoking status, HBV status, hypertension, COPD status, asthma status, CKD status, diabetes, anemia, thrombopenia, ascites, pneumonia, liver cirrhosis, HE, portal vein invasion, microvascular invasion, N stage, T stage, therapy type and AFP.The optimal lambda value (λ.min) determined through cross-validation was 0.009, indicating the point at which the model's performance is optimized by balancing model complexity and prediction accuracy (Table 3).

Multivariate logistic regression analysis
The 21 predictors identified by LASSO regression were further analysed using multivariate logistic regression to determine their independent associations with in-hospital mortality.This step was crucial to refine the model by identifying the most robust predictors while adjusting for potential confounders.The multivariate logistic regression analysis revealed that 10 variables-LOS, HBV status, hypertension, COPD status, anemia, thrombopenia, liver cirrhosis status, HE status, N stage, and microvascular invasion-were independently associated with in-hospital mortality (p < 0.05).These findings are detailed in Table 4 and underscore the significance of these ten factors as independent clinical predictors of hospitalization-related mortality in HCC patients following interventional therapy.

Development of an individualized prediction model
Utilizing the ten variables identified by logistic regression analysis-LOS, HBV infection, hypertension, COPD, anemia, thrombopenia, liver cirrhosis, HE, N stage, and microvascular invasion-a nomogram was constructed to facilitate the prediction of mortality risk during hospitalization in HCC patients following interventional  therapy (Fig. 3).The nomogram is a graphical representation of a predictive model that generates a numerical probability of a clinical event, such as in-hospital mortality.Each predictor variable is assigned a point value based on its contribution to the outcome.The points for all predictors are summed to generate a total score, which corresponds to the probability of in-hospital mortality.To use the nomogram, locate each predictor variable on its corresponding axis, draw a vertical line to the "Points" axis to determine the point value, sum the points for all predictors, and then locate the total points on the "Total Points" axis to find the predicted probability of mortality.www.nature.com/scientificreports/(Fig. 4B), and in the validation set, the AUC was 0.862 (95% CI = 0.728-0.994)(Fig. 4C).For the overall dataset, the AUC was 0.908 (95% CI = 0.859-0.956)(Fig. 4A).These high AUC values indicate excellent discriminatory ability of the model.The bootstrapping validation further substantiated these findings, yielding a C-index of 0.908 for the overall dataset.Additionally, Fig. 5 presents the AUC for the nomogram compared to the AUC for each individual predictor included in the model.This comparison illustrates that the nomogram outperforms individual predictors, emphasizing its comprehensive predictive power.The AUC values for individual predictors in the overall dataset were: LOS (0.606), HBV (0.605), hypertension (0.618), COPD (0.624), anemia (0.577), thrombopenia (0.598), liver cirrhosis (0.631), HE (0.601), microvascular invasion (0.624), and N stage (0.630).The nomogram's AUC was significantly higher at 0.908 (Fig. 5A).In the training set, the AUC values for individual predictors were: LOS (0.600), HBV (0.571), hypertension (0.592), COPD (0.619), anemia (0.574), thrombopenia (0.592), liver cirrhosis (0.630), HE (0.612), microvascular invasion (0.665), and N stage (0.632).The nomogram's AUC in the training set was 0.926 (Fig. 5B).In the validation set, the AUC values for individual predictors were: LOS (0.629), HBV (0.686), hypertension (0.677), COPD (0.636), anemia (0.587), thrombopenia (0.613), liver cirrhosis (0.632), HE (0.574), microvascular invasion (0.528), and N stage (0.625).The nomogram's AUC in the validation set was 0.862 (Fig. 5C).Specific details of each model are provided in Table 5.

Calibration of the predictive model
Calibration analysis assessed the accuracy of predicted probabilities against observed outcomes across the training (N = 242), validation (N = 103), and overall (N = 345) datasets.In the training dataset, the model showed good calibration with a mean absolute error (MAE) of 0.027 and a mean squared error (MSE) of 0.00209, with 90%  www.nature.com/scientificreports/ of predictions within an absolute error of less than 6.9% (Fig. 6B).The validation dataset had a slightly higher MAE of 0.039 and an MSE of 0.00274, with 90% of predictions within 7.1% of actual outcomes (Fig. 6C).The overall dataset demonstrated similar calibration to the training set, with an MAE of 0.027, an MSE of 0.00236, and 90% of predictions within 9.2% of actual outcomes (Fig. 6A).

Clinical use
The Decision Curve Analysis (DCA) of the nomogram assessed the clinical utility for predicting in-hospital death.The nomogram was beneficial for threshold probabilities of 1%-25% in the training set, 1%-15% in the validation set, and 1%-25% in the overall dataset.In the training set, the nomogram provided a higher net benefit than treating all or no patients within a threshold range of 0.01 to 0.20, with the greatest benefit at a threshold probability of 0.10 (Fig. 6E).In the validation set, the nomogram demonstrated superior net benefit within a threshold range of 0.01 to 0.15, with the maximum net benefit at approximately 0.05 (Fig. 6F).The overall dataset DCA showed consistent net benefit across a threshold range of 0.01 to 0.25, with the highest benefit at around 0.10 (Fig. 6D).These findings highlight the model's effectiveness in guiding clinical decision-making and optimizing patient outcomes.The Clinical Impact Curve (CIC) illustrated the clinical impact of the nomogram by showing the number of high-risk patients and high-risk events at various threshold probabilities.In the training set, the number of high-risk patients decreased from 242 at a threshold of 0.00 to 20 at a threshold of 0.50, while high-risk events ranged from 28 to 12 (Fig. 6H).The validation set showed similar trends, with high-risk patients decreasing from 103 to 8 and high-risk events from 12 to 3 (Fig. 6I).The overall dataset CIC showed high-risk patients decreasing from 345 to 28 and high-risk events from 40 to 15 (Fig. 6G).This analysis supports the nomogram's clinical utility and reliability, accurately identifying high-risk patients and predicting high-risk events.

Risk groups
Predicted probabilities of in-hospital mortality were calculated for all patients using the nomogram.A threshold of 0.20 was selected to define high-risk and low-risk groups based on the Youden Index and validated using the testing dataset.Patients with probabilities above 0.20 were classified as high-risk, while those below 0.20 were  low-risk.This classification resulted in 89 high-risk (25.8%) and 256 low-risk (74.2%) patients.Observed inhospital mortality rates were 28.1% for high-risk and 6.3% for low-risk groups, highlighting the nomogram's effectiveness in identifying at-risk patients.The risk stratification approach was evaluated using the AUC-ROC and decision curve analysis (DCA).The AUC-ROC for the risk groups was 0.844 (95% CI = 0.787-0.901),indicating good discriminatory ability.DCA showed a higher net benefit for the risk-based approach compared to treating all or no patients across various threshold probabilities, confirming its clinical utility.

Discussion
Patients with HCC who cannot undergo surgical resection often present with varying degrees of comorbidity burdens 20,21 .These include multimorbidity disease related to the liver tumour itself as well as nonhepatic comorbidities, such as hypertension, diabetes, and renal failure 21,22 .In our study, a diverse range of comorbid conditions accompanying HCC were exhibited in substantial proportions of patients.Specifically, 46.4% of the patients had HBV infection, 46.7% presented with hypertension, 40.6% had COPD, 37.7% had thrombocytopenia, 11.3% had anaemia, 31.9% had liver cirrhosis, and 32.2% manifested HE.
Notably, 46.1% of the HCC patients in our cohort were older than 65 years, suggesting a potential correlation between advanced age and increased comorbidity incidence.The increasing complexity of comorbidities with age underscores the need for a deeper understanding of how age-related factors contribute to the comorbidity burden in HCC patients 21,23,24 .This trend highlights the multifaceted challenges in managing HCC, particularly in the context of an ageing population.The presence and type of these comorbidities are key factors affecting the adverse prognosis in HCC patients 22 .The variety and cumulative effect of these comorbidities can increase the risk of longer hospital stays, in-hospital mortality, and postoperative complications for HCC patients 25 .In our cohort of HCC patients following interventional therapy, deaths were recorded during hospitalization, corresponding to an in-hospital mortality rate of 11.59%.Logistic regression analysis revealed that the use of TACE, RT, or RFA as treatment modalities was not significantly associated with the risk of in-hospital mortality (p = 0.667).These findings suggest that the choice among these therapeutic interventions may not significantly influence immediate survival outcomes in hospitalized HCC patients, underscoring the need for further investigation into other factors influencing mortality.Additionally, the severity of these comorbidities plays a crucial role in determining the choice of treatment modality for HCC patients 26 .
Among the 40 patients who experienced in-hospital mortality, the causes of death were distributed as follows: 12 patients (30%) died of liver failure and hepatic encephalopathy, 6 patients (15%) died of sepsis, 2 patients (5%) died of pneumonia, 8 patients (20%) died of multi-organ failure, 6 patients (15%) died of acute respiratory distress syndrome (ARDS), 4 patients (10%) died of gastrointestinal hemorrhage, 1 patient (2.5%) died of diabetic ketoacidosis, and 1 patient (2.5%) died of hyperosmolar hyperglycemic state.The relationship between chronic comorbidities, advanced HCC, and interventional therapies is complex and multifaceted, significantly contributing to the increased risk of in-hospital mortality observed in this study.The majority of deaths from liver failure and hepatic encephalopathy were observed in patients with advanced HCC and significant liver cirrhosis.Interventional therapies such as TACE, RT, and RFA, while aimed at controlling tumor growth, can further compromise already impaired liver function, leading to decompensation and failure.Infections, including sepsis and pneumonia, were significant causes of mortality, particularly in patients with chronic conditions such as COPD, diabetes, and CKD, who are more susceptible to infections due to their compromised immune systems.Multi-organ failure, often a terminal event in patients with advanced cancer and multiple comorbidities, was a substantial cause of death, highlighting the need for meticulous monitoring and supportive care.ARDS, resulting from both direct lung injury and indirect causes, was a notable cause of death, especially in patients with pre-existing respiratory conditions.Gastrointestinal hemorrhage, often associated with portal hypertension in advanced liver disease, further increased mortality risk, particularly in patients with thrombopenia and anemia.Although less common, diabetic ketoacidosis and hyperosmolar hyperglycemic state were fatal complications in two patients, underscoring the metabolic vulnerability of patients with diabetes undergoing stressful procedures.
These findings emphasize the significant impact of chronic comorbidities on the outcomes of HCC patients undergoing interventional therapy.The presence of multiple chronic conditions exacerbates the risks associated with advanced cancer and invasive treatments, leading to higher in-hospital mortality rates.This comprehensive understanding of mortality causes underscores the necessity for integrated, multidisciplinary care approaches to address both the oncological and comorbid aspects of patient health, ultimately aiming to improve patient outcomes and quality of care.
Currently, there is a lack of comprehensive research on the impact of multiple comorbidities on the shortterm prognosis of hospitalized HCC patients after interventional treatments.To address this gap, we conducted a retrospective exploratory study and selected 345 patients with HCC who were hospitalized for interventional treatments.Our focus was on assessing and identifying risk factors that could increase the likelihood of inhospital mortality, and we developed a comprehensive nomogram integrating comorbid diseases (including HBV, hypertension, COPD, anaemia, thrombopenia, liver cirrhosis, and HE), LOS, N stage, and microvascular invasion.This tool demonstrated remarkable proficiency in predicting the risk of in-hospital death among HCC patients receiving interventional therapy.The excellent discrimination and calibration of the nomogram, coupled with DCA, underscore its potential clinical utility.Existing models for predicting outcomes in HCC primarily focus on tumour characteristics and liver function 11,27,28 .Our study adds a novel dimension by incorporating multimodal diseases, which are often overlooked.This approach aligns with growing evidence suggesting that comorbid conditions significantly impact cancer prognosis and treatment responses 4,20,[29][30][31] .Unlike the previous models for predicting admission and functional decline 9 , our nomogram offers a more holistic view of patient health status to predict hospitalization death.The inclusion of multimodal diseases in our nomogram reflects the complex interplay between HCC and systemic health.Conditions such as HBV infection and liver cirrhosis directly impact liver cancer progression 29 , while comorbidities such as hypertension and COPD may complicate patient management during interventional therapies 32,33 .Our model suggests the need for a more integrated approach in treating HCC, where comorbidities are not mere footnotes but are central to planning and prognosis.The application of this nomogram in clinical settings could revolutionize patient assessment and personalized care.By accurately predicting in-hospital mortality risks, clinicians can make more informed decisions regarding intervention strategies, potentially improving survival outcomes and patient quality of life.

Limitations
The interpretation of this study's findings must consider both its strengths and limitations.One notable strength is the extensive dataset, comprising a large cohort of HCC patients who underwent palliative locoregional therapy at a reputable university teaching hospital, ensuring high diagnostic accuracy.However, several limitations warrant attention.As a retrospective cross-sectional study, it captures only a single point in time, precluding longitudinal follow-up and limiting our ability to establish causal relationships and monitor long-term treatment effects.Additionally, the specificity of the patient population and clinical setting may restrict the generalizability of our findings to broader contexts, necessitating external validation in diverse cohorts.The reliance on routinely collected hospital administrative data might lead to the underreporting of comorbidities, resulting in a not common and heterogeneous population with conditions such as cirrhosis, potentially underestimating their impact on mortality risk.Furthermore, the predictive model was developed using single-center data, underscoring the Vol:.(1234567890

Conclusion
Our study emphasizes the importance of considering multimorbidity in the prognosis of HCC patients after interventional therapy and introduces a novel tool for predicting the risk of in-hospital death.This nomogram shows potential for enhancing patient-specific management strategies, ultimately contributing to improved outcomes in HCC patients.

Figure 1 .
Figure 1.Flow diagram of the selection of eligible patients.

Figure 4
Figure 4 illustrates the robust discriminative capability of the model as evidenced by the performance of the nomogram in predicting mortality during hospitalization.The effectiveness of our prediction nomogram was quantified by the C-index and AUC-ROC.In the training set, the AUC was 0.926 (95% CI = 0.883-0.968)

Figure 2 .
Figure 2. Demographic and clinical feature selection using LASSO binary logistic regression model.

Figure 4 .
Figure 4. ROC curves for the training and validation set.overall set (A); training set (B); validation set (C).

Table 2 .
Baseline Characteristics of Patients with HCC Following Interventional Therapy in Overall, Training, and Validation Cohorts.

Table 3 .
Coefficients and lambda.minvalue of the LASSO regression.

Table 4 .
Multivariate Logistic Regression Analysis of Variables in the Training Cohort.

Table 5 .
AUC of overall dataset, training dataset and validation dataset.